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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Keratinophilic fungi: Specialized fungal communities in a desert ecosystem identified using cultured-based and Illumina sequencing approaches

Soil fungi in desert ecosystems are adapted to harsh environmental conditions such as high soil surface temperatures and limited organic matter and water. Given limited carbon inputs from plant material, heterotrophic fungi likely use unconventional sources of carbon in these systems. A baiting method was used to culture keratinophilic fungi from biocrust and rhizosphere soils in an arid grassland in Utah, USA. Fungi were baited using llama and sheep wool, horsehair, and snakeskin on two media, and pure cultures were identified using ITS and LSU rRNA sequences. One hundred-eighteen fungal colonies were grown, representing a total of 32 Operational Taxonomic Units (OTUs) at 97 % similarity. Cultures were dominated by the phylum Ascomycota (88 %) followed by Mucoromycota (8.6 %) and Basidiomycota (3.4 %). The orders Pleosporales, Eurotiales, Hypocreales, and Sordariales were commonly isolated, with the dominant taxa Alternaria (27 %), Aspergillus (22 %), Fusarium (11 %), and Chaetomium (8%). Thirty percent of the fungi isolated have the capacity to degrade keratin in vitro using a keratin azure assay, with Penicillium showing the highest degradation followed by Geomyces, Alternaria, and Fusarium. Although keratin degraders can be infectious, dermatophytes associated with skin infections were not isolated in culture or detected in Illumina sequencing. Illumina sequencing was used to determine general patterns in seasonal variation and habitat preference of keratinophiles. Alternaria was the most abundant genus with >70 % of the sequences. The combination of Illumina data with culture-dependent approaches facilitated the characterization of a specialized community and confirmed the low abundance of dermatophytes in this arid site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fork Experiments in the Hot Cell Using Spent Fuel Rods for International Nuclear Safeguards

This work leveraged the rare availability of 25 full-length pressurized water reactor spent fuel rods and 1 irradiated mixed-oxide rod at an Oak Ridge National Laboratory hot cell. This was done to collect measurement data with two Fork detectors to assess the detectors’ capability of verifying operator declaration data and detecting partial defects in spent fuel, which are the two primary goals of international safeguards on spent nuclear fuel. The data can also be used to benchmark the ORIGEN module, which has been adopted in the International Atomic Energy Agency’s (IAEA’s)/European Atomic Energy Community’s (Euratom’s) Integrated Review and Analysis Program to predict the Fork detector count rates in real time. In this project, the authors first calibrated two Fork detectors—a standard one and a modified one—by using known strong neutron and gamma sources. Then, the authors measured all 26 fuel rods at multiple locations along the length. The fuel rods were then assembled into three arrays—2 × 2, 3 × 3, and 5 × 5—by using specially designed support grids to mimic fuel assemblies and measure the arrays with both detectors. For the 5 × 5 array, 4 and 8 fuel rods of the array were replaced in two separate cases with short stainless-steel rods to mimic two partial defect scenarios, and the arrays were measured before and after the replacements. Polyethylene blocks were used in this experiment to mimic water. The results show that the Fork detectors were able to verify operator declarations and detect partial defects in spent fuel, and the authors were the first to demonstrate this through experiments. A discovery was also made that determined the root cause of the nonlinear response to gamma dose in the ion chambers used in IAEA and Euratom’s Fork detectors. After the experiments, both detectors were retrieved from the hot cell for future use. The data collected in this project will be used in a parallel International Nuclear Safeguards Engagement Program (INSEP) project to enhance the safeguards in the Finnish spent fuel encapsulation plant, and the data will be useful to other projects in the future given the increased safeguards needs due to spent fuel transfer and disposal activities worldwide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Giant Virus Infection Signatures Are Modulated by Euphotic Zone Depth Strata and Iron Regimes of the Subantarctic Southern Ocean

Viruses can alter the abundance, evolution, and metabolism of microorganisms in the ocean, playing a key role in water column biogeochemistry and global carbon cycles. Large efforts to measure the contribution of eukaryotic microorganisms (e.g., protists) to the marine food web have been made, yet the in situ activities of the ecologically relevant viruses that infect these organisms are not well characterized. Viruses within the phylum Nucleocytoviricota (“giant viruses”) are known to infect a diverse range of ecologically relevant marine protists, yet how these viruses are influenced by environmental conditions remains under-characterized. By employing metatranscriptomic analyses of in situ microbial communities along a temporal and depthresolved gradient, we describe the diversity of giant viruses at the Southern Ocean Time Series (SOTS), a site within the subpolar Southern Ocean. Using a phylogeny-guided taxonomic assessment of detected giant virus genomes and metagenome-assembled genomes, we observed depth-dependent structuring of divergent giant virus families mirroring dynamic physicochemical gradients in the stratified euphotic zone. Analyses of transcribed metabolic genes from giant viruses suggest viral metabolic reprogramming of hosts from the surface to a 200-m depth. Lastly, using on-deck incubations reflecting a gradient of iron availability, we show that modulating iron regimes influences the activity of giant viruses in the field. Specifically, we show enhanced infection signatures of giant viruses under both iron-replete and iron-limited conditions. Collectively, these results expand our understanding of how the water column’s vertical biogeography and chemical surroundings affect an important group of viruses within the Southern Ocean

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

JPL VLBI Analysis Center IVS Annual Report for 2004

This report describes the activities of the JPL VLBI analysis center for the year 2004. We continue to be celestial reference frame, terrestrial reference frame, earth orientation, and spacecraft navigation work using the VLBI technique. There are several areas of our work that are undergoing active development. In 2004 we demonstrated 1 mm level troposphere calibration on an intercontinental baseline. We detected our first X/Ka (8.4/32 GHz) VLBI fringes. We began to deploy Mark 5 recorders and to interface the Mark 5 units to our software correlator. We also have actively participated in the international VLBI community through our involvement in six papers at the February IVS meeting and by collaborating on a number of projects such as densifying the S/X celestial frame creating celestial frames at K (24 GHz) and Q-bands ($# GHz)>

Jacobs, Chris↗
DOE OSTI2021

Strongly Interacting Matter Under Rotation: An Introduction

Ultrarelativistic collisions between heavy nuclei briefly generate the quark-gluon plasma (QGP), a new state of matter characterized by deconfined par- tons last seen microseconds after the Big Bang. The properties of the QGP are of intense interest, and a large community has developed over several decades, to pro- duce, measure and understand this primordial plasma. The plasma is now recog- nized to be a strongly-coupled fluid with remarkable properties, and hydrodynamics is commonly used to quantify and model the system. An important feature of any fluid is its vorticity, related to the local angular momentum density; however, this degree of freedom has received relatively little attention because no experimental signals of vorticity had been detected. Thanks to recent high-statistics datasets from experiments with precision tracking and complete kinemetic coverage at collider energies, hyperon spin polarization measurements have begun to uncover the vor- ticity of the QGP created at the Relativistic Heavy Ion Collider. The injection of this new degree of freedom into a relatively mature field of research represents an enormous opportunity to generate new insights into the physics of the QGP. The community has responded with enthusiasm, and this book (to be published as a volume of Lecture Notes in Physics series by Springer) represents some of the diverse lines of inquiry into aspects of strongly interacting matter under rotation

Becattini, Francesco↗
DOE OSTI2020

Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes

In this study, we used shotgun metagenomic sequencing to characterise the microbial metabolic potential for lignocellulose transformation in the gut of two colonies of Argentine higher termite species with different feeding habits, Cortaritermes fulviceps and Nasutitermes aquilinus. Our goal was to assess the microbial community compositions and metabolic capacity, and to identify genes involved in lignocellulose degradation. Individuals from both termite species contained the same five dominant bacterial phyla (Spirochaetes, Firmicutes, Proteobacteria, Fibrobacteres and Bacteroidetes) although with different relative abundances. However, detected functional capacity varied, with C. fulviceps (a grass-wood-feeder) gut microbiome samples containing more genes related to amino acid metabolism, whereas N. aquilinus (a wood-feeder) gut microbiome samples were enriched in genes involved in carbohydrate metabolism and cellulose degradation. The C. fulviceps gut microbiome was enriched specifically in genes coding for debranching- and oligosaccharide-degrading enzymes. These findings suggest an association between the primary food source and the predicted categories of the enzymes present in the gut microbiomes of each species. To further investigate the termite microbiomes as sources of biotechnologically relevant glycosyl hydrolases, a putative GH10 endo-β-1,4-xylanase, Xyl10E, was cloned and expressed in Escherichia coli. Functional analysis of the recombinant metagenome-derived enzyme showed high specificity towards beechwood xylan (288.1 IU/mg), with the optimum activity at 50 °C and a pH-activity range from 5 to 10. These characteristics suggest that Xy110E may be a promising candidate for further development in lignocellulose deconstruction applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs

Motivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Next Breakthroughs in Neutrino Physics (LDRD Final Report)

The neutrino is an important fundamental particle, one of the building blocks of the universe. Abetter understanding of the neutrino will answer questions regarding the origin of mass, the matter and anti-matter asymmetry of the universe, and the nature of dark matter. The nation's research community has recognized that the answers to these and other questions are within reach and has assigned neutrino experiments the highest priority for both nuclear and particle physics programs. We are investigating three aspects of neutrino experimentation: detection techniques, target materials, and data analysis. Our efforts targeted multiple applications including experiments to measure neutrinoless double-beta-decay, neutrino-oscillation and neutrino mass. Our research objectives include (1) developing approaches that make detectors scalable to larger sizes and insensitive to background signals; (2) increasing the signal strength and reducing noise from targets; and (3) efficiently distinguishing background noise from signals during analysis. In this LDRD we have advanced all of these areas. We have demonstrated the scale up of a metal organic framework that can adsorb xenon directly from the air that will allow for large neutrino detectors made from xenon. We have studied the use of Cherenkov radiation to reduce the signal backgrounds. We demonstrated the cracking of hydrogen to make atomic tritium for neutrino mass measurements, and lastly we demonstrated the benefits of machine learning techniques to improve signal to noise during analysis

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Case report: Fatal Legionella infection diagnosed via by metagenomic next-generation sequencing in a patient with chronic myeloid leukemia

Legionella is an aerobic, gram-negative, intracellular pathogen and is an important cause of community-acquired pneumonia. Legionella pneumophila is the most common causative agent of Legionella pneumonia. Clinical diagnosis of Legionella pneumonia is challenging due to the lack of specific clinical manifestations and the low positive rates of conventional pathogen detection methods. In this study, we report a case of a patient with chronic myeloid leukemia who developed rigors and high fever after chemotherapy and immunotherapy. Chest computed tomography revealed consolidation in the left lower lobe of the lung and ground-glass opacities in both lower lobes. Multiple blood cultures showed Escherichia coli , Staphylococcus aureus , Bacillus licheniformis , and positive results in the β-D-glucan test (G test). The patient was treated with various sensitive antimicrobial agents, including meropenem plus fluconazole, meropenem plus carpofungin, and vancomycin. Unfortunately, the patient’s condition gradually worsened and eventually resulted in death. On the following day of death, metagenomic next-generation sequencing (mNGS) of 1whole blood revealed L. pneumophila pneumonia with concurrent bloodstream infection (blood mNGS reads 114,302). These findings suggest that when conventional empirical antimicrobial therapy proves ineffective for critically ill patients with pneumonia, the possibility of combined Legionella infection must be considered, and mNGS can provide a diagnostic tool in such cases.

Bu, Chunhong↗
NASA NTRS1973

Noise and blast

Noise and blast environments are described, providing a definition of units and techniques of noise measurement and giving representative booster-launch and spacecraft noise data. The effects of noise on hearing sensitivity and performance are reviewed, and community response to noise exposure is discussed. Physiological, or nonauditory, effects of noise exposure are also treated, as are design criteria and methods for minimizing the noise effects of hearing sensitivity and communications. The low level sound detection and speech reception are included, along with subjective and behavioral responses to noise.

Hodge, D. C.↗
NASA NTRS1986

Patterns of vegetation in the Owens Valley, California

Spectral characteristics of semi-arid shrub communities were examined using Airborne Imaging Spectrometer (AIS) data collected in the tree mode on 23 May 1985. Mesic sites with relatively high vegetation density and distinct zonation patterns exhibited greater spectral signature variations than sites with more xeric shrub communities. Spectral signature patterns were not directly related to vegetation density or physiognomy, although spatial maps derived from an 8-channel maximum likelihood classification were supported by photo-interpreted surface features. In AIS data, the principal detected effect of shrub vegetation on the alluvial fans is to lower reflectance across the spectrum. These results are similar to those reported during a period of minimal physiological activity in autumn, indicating that shadows cast by vegetation canopies are an important element of soil-vegetation interaction under conditions of relatively low canopy cover.

Ustin, S. L.↗
NASA NTRS2014

Structural Health Monitoring on Turbine Engines Using Microwave Blade Tip Clearance Sensors

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA) has taken a lead role in the investigation of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. As part of this effort, microwave sensor technology has been investigated as a means of making high temperature non-contact blade tip clearance, blade tip timing, and blade vibration measurements for use in gas turbine engines. This paper presents a summary of key results and findings obtained from the evaluation of two different types of microwave sensors that have been investigated for use possible in structural health monitoring applications. The first is a microwave blade tip clearance sensor that has been evaluated on a large scale Axial Vane Fan, a subscale Turbofan, and more recently on sub-scale turbine engine like disks. The second is a novel microwave based blade vibration sensor that was also used in parallel with the microwave blade tip clearance sensors on the experiments with the sub-scale turbine engine disks.

Gas Turbine Engines↗
NASA NTRS2018

New Sensors and Techniques for Aircraft Engine Health Monitoring

The ability to monitor the health of the rotating components in aircraft engines is of major interest to aero community in improving safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, able to operate in a high temperature environment, and sensitive enough to detect minute changes and hidden flaws before undesired events occur. The National Aeronautics and Space Administration (NASA), through several Aviation Safety Programs, has taken a leadership role in the development of new sensor technologies and techniques for the in-situ health monitoring of aircraft turbine engines. Along with the development of new instrumentation for aircraft engines, the Glenn Research Center has a diverse set of ground test facilities that are used to simulate the conditions that a vehicle would see in flight allowing new concepts to be safely developed and evaluated on the ground before progressing to actual flight. In this presentation Mark will give an overview of the NASA Glenn Research Center, discuss its past aviation safety initiatives, present some select sensors that have been developed for aircraft engine safety, and then conclude with some new measurement techniques that have been developed for use at the NASA Glenn test facilities, enabling better measurement and simulation of flight conditions. He will discuss current research and development efforts that he is working on at the NASA Glenn Research Center in the areas of new sensors and techniques for aircraft engine health monitoring. This work is being conducted in support of aviation safety.

Turbine↗
DOE OSTI2020

Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules

The soil microbiome is an invaluable component of the biosphere and critical for ecosystem functions, including biogeochemical cycling, soil-atmosphere gas exchange, degradation of toxic compounds, and promotion of plant growth and stress resistance/resilience. An improved understanding of the soil microbiome will help with predicting how these processes respond to external perturbations, and with harnessing beneficial aspects of the soil microbiome for agronomic applications such as crop amendments. However, the extensive taxonomic and functional diversity inherent within the soil microbiome hinders efficient analysis of this system. Microbial biodiversity in soils is orders of magnitude greater than other commonly studied systems such as the human gut microbiome (Blum, Zechmeister-Boltenstern, and Keiblinger 2019; Berendsen, Pieterse, and Bakker 2012). Thousands of microbial taxa may be found in a single gram of soil (Roesch et al. 2007), and high rates of gene flow and mutation further promote microbial diversification (Sergaki et al. 2018). Concomitantly, functional diversity in soil is similarly extensive. Soil is a heterogeneous mixture of microenvironments with defined physical and chemical attributes (Bach et al. 2018), within which numerous microbial guilds of distinct life-strategies and metabolic capacities can be found (Perez-Garcia, Lear, and Singhal 2016; H.-S. Song et al. 2014). Furthermore, the soil microbiome harbors a significant fraction of rare and/or quiescent members (Blagodatskaya and Kuzyakov 2013) that exist below the threshold of detection of current technologies. Such rare taxa may make significant contributions to process rates (Shade and Gilbert 2015; Dawson et al. 2017), but their scarcity complicates their identification and analysis. Finally, the vast amounts of information generated through holistic analyses of soil communities represent an intense computational burden (Scholz, Lo, and Chain 2012; Prosser 2015) that precludes assessment of the complete functional and taxonomic diversity contained in this ecosystem.

Naylor, Dan T.↗
DOE OSTI2022

Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
DOE OSTI2022

Snowmass Instrumentation Frontier IF08 Topical Group Report: Noble Element Detectors

Particle detectors making use of noble elements in gaseous, liquid, or solid phases are prevalent in neutrino and dark matter experiments and are also used to a lesser extent in collider-based particle physics experiments. These experiments take advantage of both the very large, ultra-pure target volumes achievable and the multiple observable signal pathways possible in noble-element based particle detectors. As these experiments seek to increase their sensitivity, novel and improved technologies will be needed to enhance the precision of their measurements and to broaden the reach of their physics programs. The areas of R&D in noble element instrumentation that have been identified by the HEP community in the Snowmass process are highlighted by five key messages: IF08-1) Enhance and combine existing modalities (scintillation and electron drift) to increase signal-to-noise and reconstruction fidelity; IF08-2) Develop new modalities for signal detection in noble elements, including methods based on ion drift, metastable fluids, solid-phase detectors and dissolved targets. Collaborative and blue-sky R&D should also be supported to enable advances in this area; IF08-3) Improve the understanding of detector microphysics and calibrate detector response in new signal regimes; IF08-4) Address challenges in scaling technologies, including material purification, background mitigation, large-area readout, and magnetization; and IF08-5) Train the next generation of researchers, using fast-turnaround instrumentation projects to provide the design-through-result training no longer possible in very-large-scale experiments. This topical group report identifies and documents recent developments and future needs for noble element detector technologies. In addition, we highlight the opportunity that this area of research provides for continued training of the next generation of scientists.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Preliminary report on applications of machine learning techniques to the Nevada play fairway analysis

We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.

58 GEOSCIENCES↗